@tstonez Hey Thomas. What are top real-world use cases you've been seeing?
~200ms response time seems almost too good to be true, how do the different services stack up? Are the API and speed key differences?

@mscccc can't speak for GOOG but for us at http://prediction.io/ it seems Recommender Systems are still one of the most popular use cases, perhaps because Mahout was the 1st library we supported, but now PredictionIO integrates MLlib, Deeplearning4j, CoreNLP we see all kinds of use cases such as Demand Prediction, Churn Analysis, Fraud Detection, Lead Scoring, Documentation Classification, Sentiment Analysis …and more.
Query response time is important but also the ability to add custom business logic, data security, flexibility to modify different parts of the ML pipeline, or "engines" in our parlance, and last but not least a strong community.
Be interesting to see how the various MLaaS offerings from MSFT/GOOG/AMZN develop but on many of these points I believe open source wins.

Google Prediction API is a great platform. If you are interested in machine learning, you should also check out MonkeyLearn, a text mining platform, where developers can get data from text using machine learning.
One of the things that set us apart from Google Prediction API, is that besides using our API for common text mining tasks like topic classification or sentiment analysis, you can create your own custom text classifier, trained with your own data, that fit your needs. We also have a nice UI where you can play and build custom algorithms without any knowledge in NLP or machine learning.
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